Vintra: AI for effective video surveillance

To solve a home invasion crime, the Police Services of Dublin (California) would had to review 70 hours of video obtained from a security camera from a neighbour’s home. This amount of footage would have meant more than three days of intense review for the team. Instead of that, they tested Vintra’s solution, which removed idle frames from static footage and showed them the crucial scenes. As a result, these 70 hours became 24 helping detectives to identify the suspect’s vehicle faster.

Although 24 hours meant an important reduction, Vintra’s founders assured that nowadays, this could be reduced to 6 hours due to the improvements the technology has experienced.

Vintra at its beginning

Vintra was born as a company at the end of 2016. It was founded by Brent Boekestein and CVC researchers Dr. Angel Sappa and Dr. Ariel Amato. They all presented certain concerns about the new deep learning techniques in the field of video analytics and therefore, starting with this first idea, they decided to create a prototype and a previous product which was successfully validated by final users.

Vintra was not the first business that these two CVC researchers set up. They first co-founded a CVC spin-off called Crowdmobile S.L together with Dr. Felipe Lumbreras; in that company they developed Knowxel, which is a kind of social network where users work in different tasks. These tasks need to be solved by a large team in a short period of time and are usually related to the need of companies and organisms to collect, analyse and process large amounts of data. With Knowxel, not only were companies able to complete their tasks swiftly but users were receiving money for the work done.

Back to Vintra, it is an AI software that helps security professionals get their job done. Vintra works with footage from all kinds of cameras and is capable to identify a wide array of key attributes within a video, including descriptive, definitive, scene and object attributes.

As this technology reduces the amount of attention required by people in charge of monitoring, it allows an improvement in terms of efficiency getting investigators to focus on other higher value tasks and money and resources spent in better analysis and resolution of cases. In fact, in the first example mentioned, the analysis of multiple day footage, allowed Dublin’s police to discover that the suspect had been casing the house with the same car three days prior to the robbery, and were also able to connect his vehicle with multiple cases in the surrounding areas. This would have been impossible without the help of this intelligent device.

The inaccuracy of manual reviews

Cameras have a great presence in multiple cities around the globe. They are used to monitor streets and traffic, to generate a sense of security and are key to crime solving. Cameras provide a vast amount of footage that plays a main role in the fight against delinquency.

Nonetheless, the fact of having an extensive amount of hours of video available, can actually be a downfall when it comes to solving a crime hurriedly. In fact, a single investigator spends between 200 and 300 hours per year reviewing video footage looking for a specific person, object or event.

Humans are not well prepared to do this task efficiently because of our limited ability for long-term singular focus. After a few minutes of video review, fatigue and video blindness cause not only distractions but cancels the viewer’s visual perception.

As Vintra’s founders explain, the device is not designed with the intention of replacing the investigator’s job, but it seeks to be a complementary tool that allows them to work more efficiently and focus attention on higher value tasks.

The future of Vintra

Nowadays, Vintra is formed by a team of 13 people. However, as Dr. Angel Sappa explained, their intention is to increase this number in the near future: “The future of Vintra is to continue growing and break into new markets. In fact, in medium term, we are evaluating the incorporation of new professionals in the field of machine learning”.

Moreover, Artificial Intelligence gets smarter and is able to recognize smaller differences the more it is presented with examples. Because of this, the device will keep improving, so that anything, from types of clothing or bags, to behaviours and emotions will be quickly and accurately searchable. Further still, it will be possible to digitally search real-time video and create alerts for people, objects, vehicles and behaviour.